Instructions to use QuantFactory/calme-2.3-phi3-4b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/calme-2.3-phi3-4b-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/calme-2.3-phi3-4b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/calme-2.3-phi3-4b-GGUF",
	filename="calme-2.3-phi3-4b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/calme-2.3-phi3-4b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/calme-2.3-phi3-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

SGLang

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/calme-2.3-phi3-4b-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/calme-2.3-phi3-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/calme-2.3-phi3-4b-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/calme-2.3-phi3-4b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/calme-2.3-phi3-4b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/calme-2.3-phi3-4b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/calme-2.3-phi3-4b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/calme-2.3-phi3-4b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/calme-2.3-phi3-4b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/calme-2.3-phi3-4b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/calme-2.3-phi3-4b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.calme-2.3-phi3-4b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/calme-2.3-phi3-4b-GGUF

This is quantized version of MaziyarPanahi/calme-2.3-phi3-4b created using llama.cpp

Original Model Card

MaziyarPanahi/calme-2.3-phi3-4b

This model is a fine-tune (DPO) of microsoft/Phi-3-mini-4k-instruct model.

⚡ Quantized GGUF

All GGUF models are available here: MaziyarPanahi/calme-2.3-phi3-4b-GGUF

🏆 Open LLM Leaderboard Evaluation Results

Detailed results can be found here

** Leaderboard 2**

Metric	Value
Avg.	23.38
IFEval (0-Shot)	49.26
BBH (3-Shot)	37.66
MATH Lvl 5 (4-Shot)	2.95
GPQA (0-shot)	9.06
MuSR (0-shot)	7.75
MMLU-PRO (5-shot)	31.42

** Leaderboard 1**

Metric	Value
Avg.	70.26
AI2 Reasoning Challenge (25-Shot)	63.48
HellaSwag (10-Shot)	80.86
MMLU (5-Shot)	69.24
TruthfulQA (0-shot)	60.66
Winogrande (5-shot)	72.77
GSM8k (5-shot)	74.53

MaziyarPanahi/calme-2.3-phi3-4b is the best-performing Phi-3-mini-4k model on the Open LLM Leaderboard. (03/06/2024).

Prompt Template

This model uses ChatML prompt template:

<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}

How to use

You can use this model by using MaziyarPanahi/calme-2.3-phi3-4b as the model name in Hugging Face's transformers library.

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline
import torch

model_id = "MaziyarPanahi/calme-2.3-phi3-4b"

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
    # attn_implementation="flash_attention_2"
)

tokenizer = AutoTokenizer.from_pretrained(
    model_id,
    trust_remote_code=True
)

streamer = TextStreamer(tokenizer)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

# this should work perfectly for the model to stop generating
terminators = [
    tokenizer.eos_token_id, # this should be <|im_end|>
    tokenizer.convert_tokens_to_ids("<|assistant|>"), # sometimes model stops generating at <|assistant|>
    tokenizer.convert_tokens_to_ids("<|end|>") # sometimes model stops generating at <|end|>
]

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)

generation_args = {
    "max_new_tokens": 500,
    "return_full_text": False,
    "temperature": 0.0,
    "do_sample": False,
    "streamer": streamer,
    "eos_token_id": terminators,
}

output = pipe(messages, **generation_args)
print(output[0]['generated_text'])

Downloads last month: 354

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/calme-2.3-phi3-4b-GGUF

Base model

microsoft/Phi-3-mini-4k-instruct

Quantized

(162)

this model

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

63.480
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

80.860
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

69.240
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

60.660
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

72.770
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

74.530
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

49.260
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

37.660
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

2.950
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

9.060
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

7.750
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

31.420